Computer vision is one of recent study areas in the computer science which studies parts corresponding to a sight vision of a machine, and there have been made continuous attempts to allow a machine to recognize the nature of an object from the information about the object and surrounding images measured by various sensors such as a camera. In this regard, the object recognition technique is one of main techniques of an intelligent robot and is directed to receiving an image of an object based on knowledge information stored by learning in advance and recognizing three-dimensional spatial information such as kind, size, orientation and location of the object in real time. This object recognition technique is a challenge not only in a simple robot field but also throughout overall computer science fields and is also one of difficult tasks which can be accomplished by gradually solving unsettled problems of the artificial intelligence.
At present, the technique for distinguishing an object by using both eyes like a human is a technique with high level of difficulty, which may not be perfectly reproduced for a considerable period. Therefore, object recognition suitable for a product robot may be performed by using a camera or applying a measurement technique such as a laser spatial sensor in a three dimension. For example, impurities may be recognized by scanning the floor, and the kind of an object may be determined using a simple three-dimensional model.
When recognizing an object, it should be preferentially solved to develop a filter for selectively detecting only an edge in a specific orientation from an image. Regarding such a filter, many researchers have been proposed new techniques as proposed in the following literatures, but they are still unsatisfactory.
(Non-patent Literature 1) D. G. Lowe, Distinctive image features from scale-invariant, key points. IJCV, 60 (2):91.110, 2004.
(Non-patent Literature 2) A. Alpher and J. P. N., Fotheringham-Smythe. Frobnication revisited. Journal of Foo, 13 (1):234-778, 2003.